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\chapter{Introduction} % Main chapter title
\label{c1_introduction} % For referencing the chapter elsewhere, use \ref{Chapter1} 
\rhead[\emph{Introduction}]{\thepage}
\lhead[\thepage]{\emph{Introduction}}
The enormity and complexity of nowadays public building constructions have  significantly increase the demand for indoor localization. For instance, in a hospital, how to locate a patient or leads them the the operation room or in a large shopping mall, how to navigate the potential customer to the shop. Solutions to those questions are largely depend on indoor localization. \\
Localization means determining location of an object inner certain area. Outdoor localization it has long been connected with Global Positioning System(GPS), since it is a mature technology with satisfiable performance. However, GPS is not a solution to indoor localization since GPS signal can't penetrate walls. Under such circumstances, GPS is not available indoor except a tiny area near the window. In the world of indoor localization, we need to find an alternative signal and the corresponding solution to resolve the location. \\
This report give a solution to indoor localization based on Wi-Fi signal fingerprint and machine learning. The location resolving, with the help of machine learning algorithm, aims to be processed in a smartphone. This technology needs Wi-Fi covered rooms but and no extra infrastructure. Considering receiving of Wi-Fi signal is simply a routine of smart phones and  computation ability in smart phones makes it possible to process machine learning algorithm, this solution is simple and practical. With only a preprogrammed machine learning application, the indoor localization problem can be solved.
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The rest of the report contains following chapters. Chapter \ref{c2_state} is about the details of state of art of indoor localization method and implementations. Meanwhile, a tool on machine learning which contains implementations of several algorithms is introduced. Chapter \ref{c3_algorithms} elaborate the essential ideas for several machine learning algorithms. Chapter \ref{c4_evaluation} shows the evaluation of performance of all demonstrated algorithms in chapter \ref{c3_algorithms} with a predefine test environment settings. Chapter  \ref{c5_implementation} contains our implementation of the machine learning based Wi-Fi fingerprint indoor localization in a Android application. It gives a brief idea how this technique is implemented in indoor localization. Chapter \ref{c6_conclusion} is the conclusion, indicating our advantage and future work.

